Indentification of chemicals in a sample using gc/saw and raman spectroscopy

ABSTRACT

A method for identification of chemicals in a sample using a gas chromatograph, a surface acoustic wave (SAW) sensor coupled with the gas chromatograph to define a gas chromatography (GC)/SAW system, and a Raman spectrometer. The method includes receiving SAW frequency response data generated by the SAW sensor, receiving Raman spectrum data generated by the Raman spectrometer, producing a Raman spectrum corresponding to an eluted component of interest based upon an integration of the Raman spectrum data, identifying a set of one or more candidate chemicals for the eluted component of interest based on a corresponding peak of the SAW frequency response data, and searching a Raman database for a match between the produced Raman spectrum and a chemical in the Raman database from among the set of candidate chemicals for the eluted component of interest.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application relates to and claims the benefit of U.S. ProvisionalApplication No. 62/339,344 filed May 20, 2016 and entitled “APPARATUSFOR SAMPLE ANALYSIS WITH MULTIPLE SENSOR FUSION,” the entire disclosureof which is hereby wholly incorporated by reference.

STATEMENT RE: FEDERALLY SPONSORED RESEARCH/DEVELOPMENT

Not Applicable

BACKGROUND Technical Field

The present disclosure relates generally to identification of chemicalsin a sample, and more particularly, to identification of chemicalsthrough the use of gas chromatography (GC), a surface acoustic wave(SAW) sensor, and a Raman spectrometer system.

Related Art

Various devices for qualitative identification and/or quantitativemeasurement of chemicals in a sample make use of gas chromatography forseparation of the sample into components. A vaporized sample passesthrough a GC column of a gas chromatograph in which different componentsof the sample are retained for different lengths of time depending ontheir chemical-physical properties. As each component elutes from the GCcolumn, its retention time is measured by a detector. Chemicalidentification of each component is based on analysis of the measuredretention time and the identified properties by the sensory technology.One of the most commonly used techniques, considered the standard inanalytical chemistry, is GC/mass spectrometry (MS), in which a massspectrometer is employed as the detector, making use of its additionalcapability of detecting ionized fragments of each separated component.However, there are some significant drawbacks of this technology, suchas large size, high cost, and the fact that GC/MS systems are typicallynot portable.

Other examples of detectors that have been used with gas chromatographyinclude photoionization detectors (PID), electron capture detectors(ECD), and SAW sensors (so-called GC/SAW systems). For these types ofdetectors, chemical identification is based solely on GC retention time,which has known limitations, such as the possibility of falseidentification in cases where components are not well separated by thegas chromatograph.

Another area of technology, conventionally unconnected to gaschromatography, is spectroscopy, in which a device measures changes inan electromagnetic spectrum before and after excitation of a sample withlight. One such device is a Raman spectrometer, which measures Ramanscattering, i.e. inelastic scattering of photons as molecules in thesample are excited to virtual energy states. Since such spectra aredirectly related to the molecular structure of the sample, chemicalidentification can be achieved with high accuracy. However, a highconcentration of the chemical is typically needed.

BRIEF SUMMARY

The present disclosure contemplates various systems, methods, andapparatuses for overcoming the above drawbacks accompanying the relatedart. One aspect of the embodiments of the invention is a method foridentification of chemicals in a sample. The method includes receivingsurface acoustic wave (SAW) frequency response data generated by a SAWsensor of a gas chromatography (GC)/SAW system, the SAW frequencyresponse data including one or more peaks corresponding respectively toone or more eluted components separated from a sample by a gaschromatograph of the GC/SAW system, receiving Raman spectrum datagenerated by a Raman spectrometer for the one or more eluted components,producing a Raman spectrum corresponding to an eluted component ofinterest from among the one or more eluted components based upon anintegration of the Raman spectrum data, identifying a set of one or morecandidate chemicals for the eluted component of interest based on thecorresponding peak of the SAW frequency response data, and searching aRaman database for a match between the produced Raman spectrum and achemical in the Raman database from among the set of candidate chemicalsfor the eluted component of interest.

Another aspect of the embodiments of the invention is a system foridentification of chemicals in a sample. The system includes a gaschromatograph, a surface acoustic wave (SAW) sensor coupled with the gaschromatograph to define a gas chromatography (GC)/SAW system in whichone or more eluted components separated from a sample by the gaschromatograph accumulate at a condensation spot on the SAW sensor, and aRaman spectrometer aligned with the condensation spot, Raman scatteredlight from one or more eluted components accumulated at the condensationspot being collectable by the Raman spectrometer. The system may furtherinclude an input interface communicatively coupled to the SAW sensor andthe Raman spectrometer, the input interface being receptive to SAWfrequency response data generated by the SAW sensor and including one ormore peaks corresponding respectively to the one or more elutedcomponents separated from the sample by the gas chromatograph, the inputinterface further being receptive to Raman spectrum data generated bythe Raman spectrometer for the one or more eluted components, a spectrumproducer communicatively coupled to the input interface, a Ramanspectrum corresponding to an eluted component of interest from among theone or more eluted components being produced by the spectrum producerbased upon an integration of the Raman spectrum data, a candidatechemical identifier communicatively coupled to the input interface, aset of one or more candidate chemicals for the eluted component ofinterest being identified by the candidate chemical identifier based onthe corresponding peak of the surface acoustic wave frequency responsedata, and a Raman search engine communicatively coupled to the candidatechemical identifier and the spectrum producer, a Raman database beingsearched by the Raman search engine for a match between the producedRaman spectrum and a chemical in the Raman database from among the setof candidate chemicals for the eluted component of interest.

Another aspect of the embodiments of the invention is a non-transitoryprogram storage medium on which are stored instructions executable by aprocessor or programmable circuit to perform operations foridentification of chemicals in a sample. The operations includereceiving surface acoustic wave (SAW) frequency response data generatedby a SAW sensor of a gas chromatography (GC)/SAW system, the SAWfrequency response data including one or more peaks correspondingrespectively to one or more eluted components separated from a sample bya gas chromatograph of the GC/SAW system, receiving Raman spectrum datagenerated by a Raman spectrometer for the one or more eluted components,producing a Raman spectrum corresponding to an eluted component ofinterest from among the one or more eluted components based upon anintegration of the Raman spectrum data, identifying a set of one or morecandidate chemicals for the eluted component of interest based on thecorresponding peak of the SAW frequency response data, and searching aRaman database for a match between the produced Raman spectrum and achemical in the Raman database from among the set of candidate chemicalsfor the eluted component of interest.

The present disclosure will be best understood accompanying by referenceto the following detailed description when read in conjunction with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the various embodimentsdisclosed herein will be better understood with respect to the followingdescription and drawings, in which like numbers refer to like partsthroughout, and in which:

FIG. 1 illustrates a system for identification of chemicals in a sampleaccording to an embodiment of the invention;

FIG. 2 illustrates an example apparatus 200 for identification ofchemicals in a sample according to an embodiment of the invention;

FIG. 3 is a graphical representation of an example of SAW frequencyresponse data;

FIG. 4 is a graphical representation of an example of integrated Ramanspectrum data with different integration times (upper plots), togetherwith a known Raman spectrum (lower plot);

FIG. 5 is an example of SAW frequency response data and Raman spectrumdata;

FIG. 6 shows an example operational flow in relation to the system shownin FIG. 1 according to an embodiment of the invention;

FIG. 7 shows an example operational flow of the apparatus 200 accordingto an embodiment of the invention;

FIG. 8 is an example operational flow of step S730 in FIG. 7;

FIG. 9A is an example operational flow of step S740 in FIG. 7;

FIG. 9B is a conceptual representation of partially overlapped peaks ofthe SAW frequency response data;

FIG. 9C is a conceptual representation of fully overlapped peaks of theSAW frequency response data;

FIG. 9D is another example operational flow of step S740 in FIG. 7;

FIG. 10 is an example operational flow of step S930 in FIGS. 9A and 9D;

FIG. 11A is an example operational flow of step S920 in FIGS. 9A and 9D;

FIG. 11B is another example operational flow of step S920 in FIGS. 9Aand 9D;

FIG. 12 is an example operational flow of step S950 in FIGS. 9A and 9D;

FIG. 13 is an example operational flow of step S1220 in FIG. 12;

FIGS. 14A and 14B show an example of a computer 1400 in which theapparatus 200 of FIG. 2, the operational flows of FIGS. 6-13, and/orother embodiments of the claimed invention may be wholly or partlyembodied, with FIG. 14A showing a computer 1400 and FIG. 14B showing ablock diagram of a system unit 1410.

DETAILED DESCRIPTION

The present disclosure encompasses various embodiments of systems,methods, and apparatuses for identification of chemicals in a sample.The detailed description set forth below in connection with the appendeddrawings is intended as a description of the several presentlycontemplated embodiments of these methods, and is not intended torepresent the only form in which the disclosed invention may bedeveloped or utilized. The description sets forth the functions andfeatures in connection with the illustrated embodiments. It is to beunderstood, however, that the same or equivalent functions may beaccomplished by different embodiments that are also intended to beencompassed within the scope of the present disclosure. It is furtherunderstood that the use of relational terms such as first and second andthe like are used solely to distinguish one from another entity withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities.

FIG. 1 illustrates a system 10 for identification of chemicals in asample according to an embodiment of the present disclosure. The system10 includes a gas chromatograph 100, which may include an injection port102 and a gas chromatography (GC) column 104. Upon being injected intothe gas chromatograph 100 via the injection port 102, a vaporized sampleis carried by a carrier gas 106 through the GC column 104, wheredifferent components of the sample are retained for different lengths oftime depending on their chemical-physical properties. The system 10illustrated in FIG. 1 further includes a surface acoustic wave (SAW)sensor 108 coupled with the gas chromatograph 100 to define a GC/SAWsystem in which one or more eluted components separated from the sampleby the gas chromatograph 100 accumulate at a condensation spot 110 onthe SAW sensor 108. The SAW sensor 108 may, for example, be arrangedclose to the output of the GC column 104 such that the condensation spot110 is close to the size of an internal dimension of the GC column 104.Chemical components of the sample eluting from the GC column 104 arriveat the condensation spot 110, where they condense as long as thetemperature of the SAW sensor 108 is lower than the dew point of thechemical component. To this end, the system may include a thermoelectriccooler 112 by which the temperature of the SAW sensor 108 can beadjusted. Electronics (not shown) associated with the SAW sensor 108 maygenerate SAW frequency response data including one or more peakscorresponding respectively to one or more chemical components thatcondense at the condensation spot 110 as they elute from the GC column104.

The system 10 of FIG. 1 further includes a Raman spectrometer 114aligned with the condensation spot 110 so as to collect Raman scatteredlight 117 from one or more eluted components accumulated at thecondensation spot 110. The head or probe 115 of the Raman spectrometer114, which houses the optical elements (e.g. lenses, filters, mirrors)of the Raman spectrometer 114, may be positioned above the SAW sensor108 at a distance of a few millimeters to a few centimeters and at anangle so as not to interfere with the accumulation of the chemicalcomponents at the condensation spot 110. The head or probe 115 may be ofcoaxial design, with one or more optical elements focusing an excitationlaser 118 introduced through an excitation optical fiber 122 at thecondensation spot 110 and thereafter receiving Raman scattered light 117from the condensation spot 110 to be collected by a collection opticalfiber 116. The positions of the GC column 104 and SAW sensor 108 may bepredetermined and fixed, allowing for high precision focusing of theexcitation laser 118 at the condensation spot 110. Raman scattered light117 collected by the collection optical fiber 116 may be subsequentlyincident on a Raman sensor (not shown), e.g. a charged coupled device(CCD) and used to generate Raman spectrum data of one or more chemicalcomponents that condense at the condensation spot 110 as they elute fromthe CD column 104. Raman spectrum data may be generated at a samplingfrequency matching that of the SAW frequency response data to producecorresponding data sets.

The gas chromatograph 100, SAW sensor 108, and Raman spectrometer 114may be of any type known in the art. Selection of the injection port102, GC column 104, and carrier gas 106 may be in accordance with knownprinciples of gas chromatography and GC/SAW systems and may depend onthe nature of the sample and the particular application. For example,the diameter of the GC column and other aspects of the GC/SAW system maybe selected to support fast GC.

Lastly, the system of FIG. 1 includes an apparatus 200 which may supportthe qualitative and/or quantitative identification of chemicals in thesample. The apparatus 200 may be operatively connected to each of theother components of the system 10, including the SAW sensor 108 and theRaman spectrometer 114, as well as other system components such as thegas chromatograph 100 and/or the thermoelectric cooler 112. Thus, theapparatus 200 may be used for post-processing of SAW frequency responsedata and Raman spectrum data and/or control of the various systemcomponents in accordance with results of processing by the apparatus 200and/or user input to the apparatus 200, in which case the apparatus 200may serve as a user terminal. The operative connection of the apparatus200 may be a physical (e.g. wired) connection, a wireless connectionover a network, or a purely conceptual connection such as in a casewhere data generated by the SAW sensor 108, Raman spectrometer 114, etc.is then accessed, processed, etc. by the apparatus 200 (e.g. after beingtransferred by some data storage medium) in the case of post-processing,or vice versa in the case of system control by the apparatus 200.

FIG. 2 illustrates an example apparatus 200 for identification ofchemicals in a sample according to an embodiment of the invention. Theapparatus 200 receives SAW frequency response data and Raman spectrumdata, integrates the Raman spectrum data to produce a Raman spectrumcorresponding to an eluted component of interest, identifies a set ofcandidate chemicals using the SAW frequency response data, and searchesa Raman database for a match between the Raman spectrum and one of thecandidate chemicals. On the basis of such search, the apparatus 200 mayidentify the chemical of the eluted component of interest (e.g. in thecase of a successful search) and/or adjust settings of the other systemcomponents of FIG. 1. The apparatus 200 includes an input interface 210,a SAW data analyzer 220, a candidate chemical identifier 230, a chemicalevaluator 250, a mass estimator 270, and an output interface 290 and mayfurther include or have access to a retention index database 240, aRaman database 260, and a calibration curve storage 280.

The input interface 210 receives SAW frequency response data generatedby the SAW sensor 108 of the GC/SAW system, the SAW frequency responsedata including one or more peaks corresponding respectively to one ormore eluted components separated from a sample by the gas chromatograph100 of the GC/SAW system. The input interface 210 further receives Ramanspectrum data generated by the Raman spectrometer 114 for the one ormore eluted components. The SAW frequency response data is arepresentation of the frequency response of the SAW sensor 108 as afunction of time and may be in the form of, for example, frequency in Hzversus GC retention time in seconds. Thus, in a case where the samplingfrequency of the SAW sensor 108 is 50 Hz, the SAW frequency responsedata may include a series of frequency response samples at 20millisecond intervals. The frequency response of the SAW sensor 108 maybe, for example, a change in an oscillation frequency due to theaccumulation of an eluted chemical component on the SAW sensor 108. TheRaman spectrum data is a representation of Raman scattered light 117received by the Raman spectrometer 114 and incident on a Raman sensorthereof and may be in the form of, for example, intensity in counts persecond as a function of Raman shift in cm−1 versus GC retention time inseconds. Thus, in a case where the sampling frequency of the Ramanspectrometer 114 is 50 Hz, the Raman spectrum data may include a seriesof Raman spectrum samples at 20 millisecond intervals.

The input interface 210 may receive the SAW frequency response data andRaman spectrum data from outside the apparatus 200. For example, theinput interface 210 may receive the SAW frequency response data andRaman spectrum data directly from the other system components of FIG. 1,e.g. by wired or wireless connection with the SAW sensor 108 and theRaman spectrometer 114 or associated electronics (not shown) thereof.Alternatively, the SAW frequency response data and Raman spectrum datacan be received from an external storage or received from a computer orserver through a network such as the Internet, WAN, and/or LAN.

The SAW data analyzer 220 identifies the one or more peaks of the SAWfrequency response data and identifies one or more valleys of the SAWfrequency response data. For example, the SAW data analyzer 220 mayidentify peaks and valleys by approximating the SAW frequency responsedata with a polynomial, taking the derivative of the polynomial, andfinding the points where the derivative of the polynomial is equal tozero (corresponding to maxima and minima of the polynomial). The SAWdata analyzer 220 may thus characterize each of the one or more peaksand each of the one or more valleys by its GC retention time or samplenumber. The SAW data analyzer 220 may further characterize each of theone or more peaks by a peak height and/or a peak area (e.g. byintegrating the polynomial approximating the SAW frequency response datafrom valley to valley around the peak).

The SAW data analyzer 220 may also evaluate whether adjacent peaks ofthe one or more peaks are partially overlapping. Partially overlappedpeaks, as distinguished from fully overlapped peaks or non-overlappedpeaks, may be those peaks that are so close together that accurate Ramanspectra of the chemical components may be difficult to produce, whilebeing far enough apart that the peaks are separately discernible by theSAW data analyzer 220. More specifically, if two peaks are so closetogether that the SAW data analyzer 220 does not identify a valleybetween them, the peaks are considered fully overlapped peaks and areindistinguishable (at this stage) from non-overlapped peaks. The SAWdata analyzer 220 does not characterize such fully overlapped peaks aspartially overlapped peaks. If two peaks are far enough apart that theSAW data analyzer 220 identifies a valley between them, the SAW dataanalyzer 220 may characterize the peaks as partially-overlapped peaks ornot based on predetermined criteria. For example, the SAW data analyzer220 may evaluate whether such adjacent peaks are partially overlappingby evaluating, with respect to the adjacent peaks, whether the height ofthe valley between the adjacent peaks exceeds a valley height threshold.If the valley separating the adjacent peaks is too high, it can beunderstood that the valley does not represent the first chemicalcomponent evaporating completely from the condensation spot 110 beforethe second chemical component begins to condense but rather that thereis some overlap during which both chemical components are present at thecondensation spot 110 (the first chemical beginning to evaporate whilethe second chemical begins to condense). As another example, the SAWdata analyzer 220 may evaluate whether adjacent peaks are partiallyoverlapping by evaluating, with respect to the adjacent peaks, whetherthe adjacent peaks are closer together than a peak distance threshold.In this way, a simple time relationship between the adjacent peaks (e.g.how many milliseconds apart) can roughly indicate whether some overlapof the chemical components is likely.

The candidate chemical identifier 230 identifies a set of one or morecandidate chemicals for an eluted component of interest based on thecorresponding peak of the SAW frequency response data. The candidatechemical identifier 230 may, for example, identify sets candidatechemicals for all of the eluted components, i.e. a set of candidatechemicals for each of the peaks found by the SAW data analyzer 220. Or,in a case where some peaks are uninteresting, the candidate chemicalidentifier 230 may identify sets of candidate chemicals for only asubset of the peaks found by the SAW data analyzer 220. The candidatechemical identifier 230 includes a retention index calculator 232 and aretention index search engine 234.

The retention index calculator 232 calculates a retention index for theeluted component of interest from the corresponding peak of the SAWfrequency response data. A GC retention time of a peak found by the SAWdata analyzer 220 may be converted into a retention index, e.g. Kovatsretention index, that is independent of the specific gas chromatograph100 and its operating conditions. The retention index calculator 232 maycalculate the retention index by known methods. For example, n-alkanesmay be injected into the gas chromatograph 100 together with the sample,and the peaks of the eluted n-alkanes may be found by the SAW dataanalyzer 220 together with the peaks corresponding to the chemicalcomponents of the sample. The retention index calculator 232 maycalculate the retention index of a given peak of the sample based on itsrelationship to the peaks of the eluted n-alkanes.

The retention index database 240 may include, for example, a table ofchemicals and corresponding known retention indices. The retention indexsearch engine 234 searches the retention index database 240 for one ormore matches between the determined retention index and chemicals in theretention index database 240. In this way, the candidate chemicalidentifier 230 may identify a set of one or more candidate chemicals foreach of the eluted components.

In the example of the candidate chemical identifier 230 shown in FIG. 2,identification of candidate chemicals may be achieved with the use ofretention indices. To this end, the candidate chemical identifier 230includes the retention index calculator 232 and the retention indexsearch engine 234 and has access to the retention index database 240.However, other methods of identifying candidate chemicals is known inthe art, such as direct comparison of retention times with retentiontimes of known chemicals eluted from the same gas chromatograph 100under similar conditions. Therefore, in some embodiments, the candidatechemical identifier 230 may omit the retention index calculator 232 andthe retention index search engine 234, and may thus have no need toaccess the retention index database 240.

The chemical evaluator 250 receives the analyzed SAW frequency responsedata from the SAW data analyzer 220 and the set of one or more candidatechemicals for an eluted component of interest (or for all or any subsetof the eluted components) from the candidate chemical identifier 230.The chemical evaluator 250 further receives the Raman spectrum data fromthe input interface 210. On the basis of these inputs, the chemicalevaluator 250 determines the identity of an eluted component of interestby matching a Raman spectrum of the component to a known spectrum of oneof the candidate chemicals for the component. The chemical evaluator 250includes a spectrum producer 252, a Raman search engine 254, and a modeselector 256.

The spectrum producer 252 integrates the Raman spectrum data to producea Raman spectrum corresponding to an eluted component of interest fromamong the one or more eluted components. For example, using the peaksand valleys found by the SAW data analyzer 220, the spectrum producer252 may integrate the Raman spectrum data from a valley immediatelypreceding the peak corresponding to the eluted component of interest toa valley immediately following the peak corresponding to the elutedcomponent of interest. For instance, if the peak of interest occurs inthe SAW frequency response data at t=5500 milliseconds, and thesurrounding valleys are at t=5000 milliseconds and t=6000 milliseconds,the spectrum producer 252 may integrate the samples of the Ramanspectrum data from a sample corresponding to t=5000 milliseconds to asample corresponding to t=6000 milliseconds. As a specific example, in acase where both the SAW frequency response data and the Raman spectrumdata are generated at the same sampling frequency of 50 Hz, the spectrumproducer 252 may integrate the Raman spectrum data from all of thesamples t=5000, t=5020, t=5040, . . . t=5980, and t=6000 to produce aRaman spectrum corresponding to the component represented by the peak att=5500.

The Raman database 260 may include, for example, a table of chemicalsand corresponding known Raman spectra. The Raman search engine 254searches the Raman database 260 for a match between the Raman spectrumproduced by the spectrum producer 252 and a chemical in the Ramandatabase 260 from among the set of candidate chemicals for the elutedcomponent of interest. In this way, the chemical evaluator 250 mayidentify the chemical of the eluted component of interest with greaterprecision than is possible by the candidate chemical identifier 230alone. Meanwhile, because the candidate chemical identifier 230 narrowsthe field of possibilities down to one or more candidate chemicals, thechemical evaluator 250 does not need to search the entire contents ofthe Raman database 260, and does not need to produce a Raman spectrumthat distinguishes the chemical component of interest from every knownRaman spectrum in the Raman database 260. Thus, time may be saved, andit may also be possible to relax settings and design parameters of theRaman spectrometer 114.

The above-described functionality of the spectrum producer 252 and Ramansearch engine 254 may represent a straightforward simple case, in whicha Raman spectrum is produced by valley-to-valley integration and a matchis easily found in the Raman database 260. This is typical when peaks ofthe SAW frequency response data are non-overlapped. In addition, theabove-described functionality may also represent a part of a procedurefor determining a chemical of an eluted component in a more complicatedcase, such as where peaks of the SAW frequency response data arepartially-overlapped or fully-overlapped.

In view of the possibility of partially-overlapped or fully-overlappedpeaks, the mode selector 256 selects between a plurality of chemicaldecision modes including a non-overlap decision mode, a partial-overlapdecision mode, and a full-overlap decision mode, or between any two ofthese three modes. For example, in a case where the SAW data analyzer220 evaluates whether adjacent peaks of the SAW frequency response dataare partially overlapping, the mode selector 256 may select, withrespect to the adjacent peaks, between a non-overlap decision mode and apartial-overlap decision mode based on a result of the evaluation by theSAW data analyzer 220. Thus, the mode selector 256 may select apartial-overlap decision mode for adjacent peaks that are evaluated asbeing partially overlapping. The mode selector 256 may select (e.g. bydefault) a non-overlap decision mode for other peaks. As described inmore detail below, the partial-overlap decision mode affects theprocedure by which the chemical evaluator 250 determines the identity ofan eluted component of interest.

As another example, after the Raman search engine 254 searches the Ramandatabase for a match between a Raman spectrum produced by the spectrumproducer 252 and a chemical in the Raman database 260, the chemicalevaluator 250 may evaluate whether a match was found. A failure to finda match may indicate that the peak in the SAW frequency response datacorresponding to the eluted component of interest is actually the resultof more than one eluted component with substantially the same GCretention time. That is, what appears to be a single peak may actuallybe a superimposition of two or more fully overlapped peaks. Thus, byevaluating whether a match is found, the chemical evaluator 250 mayevaluate whether the peak corresponding to the eluted component ofinterest is a combination of two or more fully overlapped peaks. Themode selector 256 may select, with respect to the peak corresponding tothe eluted component of interest, between a non-overlap decision modeand a full-overlap decision mode based on a result of the evaluation bythe chemical evaluator 250. Thus, the mode selector 256 may select afull-overlap decision mode for a peak that is evaluated as being acombination of two fully overlapping peaks. The mode selector 256 mayselect (e.g. by default) a non-overlap decision mode for other peaks. Asdescribed in more detail below, the full-overlap decision mode affectsthe procedure by which the chemical evaluator 250 determines theidentity of an eluted component of interest.

A failure to find a match may indicate scenarios other than fullyoverlapped peaks. For example, it may indicate that the Raman database260 is incomplete or that one or more of the system components (e.g. thegas chromatograph 100, the SAW sensor 108, the Raman spectrometer 114,the thermoelectric cooler 112, etc.) is not functioning correctly or hasinsufficient capability or settings (e.g. sampling frequency,resolution, signal-to-noise, etc.) to identify the chemical component.Thus, an evaluation by the chemical evaluator 250 that a match has notbeen found may also be a basis for adjusting various settings of thesystem 10 shown in FIG. 1. For example, the apparatus 200 mayautomatically adjust system settings or may produce an error report tobe acted on by some external system or user.

The mass estimator 270 estimates a mass of an eluted component ofinterest based on the corresponding peak of the SAW frequency responsedata. As explained above, the SAW data analyzer 220 may characterizeeach of the one or more peaks of the SAW frequency response data by apeak height and/or a peak area. Meanwhile, the calibration curve storage208 may store calibration curves for various chemicals, each calibrationcurve mapping peak height or peak area to mass or other quantitativemeasure (e.g. concentration) in a known relationship for that chemical.Upon the successful identification of the eluted component of interestby the chemical evaluator 250, the mass estimator 270 may compare thepeak (e.g. height or area) corresponding to the eluted component ofinterest to a calibration curve stored in the calibration curve storage280, thereby producing an estimate of the mass or other quantitativemeasure of the eluted component of interest.

The output interface 290 outputs one or more of the various outputs ofthe apparatus 200 for use by a downstream device or user. For example,the outputs may be stored, uploaded to a server, printed, or otherwisemade available for viewing or analysis. The various outputs of theapparatus 200 include, for example, singly or in combination, anidentification of one or more chemical components (e.g. an elutedcomponent of interest) of the sample as determined by the chemicalevaluator 250, a mass or other quantitative measure of one or moreidentified chemical components of the sample as estimated by the massestimator 270, the analyzed SAW frequency response data from the SAWdata analyzer 220, error reports related to failed match attempts, etc.Such outputs may also be displayed on a screen in relation to a userquery as an intermediate step in a process performed by the apparatus200.

Outputs from the output interface 290 may further include controlcommands issued by the apparatus 200 directly to other system componentsof the system shown in FIG. 1. For example, the apparatus 200 may, viathe output interface 290, adjust a temperature of the SAW sensor 108with the thermoelectric cooler 112 based on a result of the evaluationby the chemical evaluator 250 as to whether a match was found in theRaman database 260. In this way, after the chemical evaluator 250 hasexhausted its efforts to determine the chemical of an eluted componentof interest (e.g. both the non-overlap and full-overlap decision modeshave failed to find a match for a peak that is not partiallyoverlapped), the apparatus 200 may lower the temperature of the SAWsensor 108 to effect a more effective condensation of the chemical(allowing for more samples of Raman spectrum data) in a subsequent runof the system. As another example, the apparatus 200 may raise thetemperature of the SAW sensor 108 to effect shorter condensation timesin a case where there is an undesirable number of partially-overlappedpeaks in the SAW frequency response data (e.g. a number ofpartially-overlapped peaks, as determined by the SAW data analyzer 220,that exceeds a threshold).

FIG. 3 is a graphical representation of an exemplary set of SAWfrequency response data. As each chemical component of the sample elutesfrom the GC column 104, the SAW sensor 108 (or associated electronics,not shown) exhibits a SAW frequency response (shown in Hz on the y-axis)due to the accumulation of the eluted chemical component on the SAWsensor 108. The shape of each peak (e.g. height and area) is determinedin part by the quantity (e.g. mass) of the component in the sample, aswell as the temperature of the SAW sensor 108 in relation to the dewpoint of the component, which affects the width of the peak as thecomponent accumulates on the SAW sensor 108 for a longer or shorterperiod of time.

The highest peak in FIG. 3 is centered at around 5500 milliseconds onthe x-axis, indicating a retention time of approximately 5500milliseconds, which may be measured from some initial time such as themoment the sample is injected into the gas chromatograph 100. Otherpeaks may correspond to other chemical components in the sample andknown chemicals injected with the sample for purposes of determiningretention indices (e.g. n-alkanes). SAW frequency response data like theexample shown in FIG. 3 may be generated by the SAW sensor 108 orassociated electronics in various forms (not necessarily as a graphicalrepresentation as shown) and thereafter or concurrently received by theinput interface 210 of the apparatus 200 for processing by the apparatus200.

FIG. 4 is a graphical representation of an example of integrated Ramanspectrum data with different integration times (upper plots), togetherwith a known Raman spectrum (lower plot). The example in FIG. 4 is for a3 nanogram sample of trinitrotoluene (TNT). The y-axis is Ramanscattering intensity in counts per second, and the x-axis is Raman shiftin cm−1. The integrated Raman spectrum data is shown for fiveintegration times, 0.25 seconds, 0.5 seconds, 1 second, 2 seconds, and 5seconds. As illustrated by the vertical lines connecting the upper plotsto the lower plot, matching of integrated Raman spectrum data to a knownRaman spectrum can be achieved by matching spikes in the spectra. In theapparatus 200, the matching as shown in FIG. 4 may be performed by theRaman search engine 254 as part of searching for a match for a peakcorresponding to an eluted component of interest.

As can be seen, matching may be achievable in this case with as littleas 2 seconds integration time, with the signal-to-noise ratiodeteriorating at 1 second and below. Typically, peak width is longerthan 2 seconds for a gas chromatography system, as determined by thelength of the GC column 104, the temperature of the gas chromatograph100, and the flow rate of the carrier gas 106. In the system shown inFIG. 1, the peak width of the SAW frequency response data generated bythe GC/SAW system determines the maximum integration time of the Ramanspectrum data for a given peak. This is because the Raman spectrum datafor a given eluted component is gathered while the eluted componentremains condensed on the SAW sensor 108 and until it evaporates. Thus, alonger than 2 seconds peak width typically means that Raman matchingshould be possible, but this may ignore the possibility of partially orfully overlapped peaks. In addition to addressing partially and fullyoverlapped peaks using partial-overlap and full-overlap decision modesto be described in more detail below, the disclosed system contemplatesfurther controlling peak width by adjusting the temperature of the SAWsensor 108 with the thermoelectric cooler 112, which may be doneautomatically by or with reference to the output of the apparatus 200 asexplained above. By adjusting the temperature of the SAW sensor 108 andthrough the use of the partial-overlap and full-overlap decision modesdescribed with respect to the chemical evaluator 250, adequateintegration time for matching is achievable.

Integrated Raman spectrum data like the example shown in the upper plotsof FIG. 4 may be the result of generating Raman spectrum data by theRaman spectrometer 114 or associated electronics, receiving the Ramanspectrum data by the input interface 210 of the apparatus 200, andintegrating the Raman spectrum data by the spectrum producer 252. TheRaman spectrum produced by integrating the Raman spectrum data may be invarious forms (not necessarily a graphical representation) for purposesof matching with known Raman spectra in the Raman database 260.

FIG. 5 is an example of SAW frequency response data and Raman spectrumdata. As explained above, the SAW frequency response data samplesgenerated by the SAW sensor 108 or associated electronics may correspondone-to-one with the Raman spectrum data samples generated by the Ramanspectrometer 114 or associated electronics. For example, the SAWfrequency response data and Raman spectrum data may be generated at thesame sampling frequency or, if not, may be aligned with each other,trimmed, interpolated, etc. after-the-fact so as to correspond withrespect to a common time dimension. Thus, as shown in FIG. 5, the dataas received by the input interface 210 (or as aligned etc. by the inputinterface 210) may be in the form of a sample of SAW frequency responsedata S (S₁, S₂, . . . , S_(n)) and a sample of Raman spectrum data R(R₁, R₂, . . . , R_(n)) for each of a plurality of retention time valuest (t₁, t₂, . . . t_(n)) for n samples (e.g. 20 millisecond periods).Each of the SAW frequency response data samples S(t_(i)) may be, forexample, a value representing a change in an oscillation frequency dueto the accumulation of an eluted chemical component on the SAW sensor108 at time t=t_(i). Each of the Raman spectrum data samples R(t_(i))may be, for example, a Raman spectrum sample (e.g. intensity in countsper second as a function of Raman shift in cm⁻¹) at time t=t_(i). Bystoring the SAW frequency response data and the Raman spectrum datacorrelated in this manner, integration of Raman spectrum data can bedefined in the SAW frequency response data domain, e.g. “from valley tovalley.”

FIG. 6 shows an example operational flow in relation to the system 10shown in FIG. 1 according to an embodiment of the invention. First, theSAW sensor 108 and the gas chromatograph 100 are arranged to form aGC/SAW system such that eluted components separated from the sample bythe gas chromatograph 100 accumulate at a condensation spot 110 on theSAW sensor 108 (S610). Then, the Raman spectrometer 114 is aligned withthe condensation spot 110 on the SAW sensor 108 so as to collect Ramanscattered light from eluted components accumulated at the condensationspot 110 (S620). Steps S610 and 620 may, of course, be reversed orperformed substantially simultaneously. Once the GC/SAW system plusRaman spectrometer 114 are set up, a sample is introduced into the gaschromatograph 100 (S630). As chemical components elute from the GCcolumn 104, SAW frequency response data and Raman spectrum data aregenerated by the SAW sensor 108 and Raman spectrometer 114,respectively, or associated electronics (S640). After the SAW frequencyresponse data and Raman spectrum data are generated, qualitative and/orquantitative analysis is performed (S650). For example, the SAWfrequency response data and Raman spectrum data may be received by theapparatus 200 (e.g. transferred by a data storage medium or transferredby a wired or wireless connection, either locally or remotely) and theapparatus 200 may determine the identity of one or more chemicalcomponents of interest using the chemical evaluator 250 and/or estimatethe mass or other quantitative measure of one or more chemicalcomponents of interest using the mass estimator 270. Lastly, thetemperature of the SAW sensor 108 or other parameter(s) of the setupconfiguration of the system of FIG. 1 may be adjusted based on theresults of the apparatus 200 and/or automatically by the apparatus 200(S660).

FIG. 7 shows an example operational flow of the apparatus 200 accordingto an embodiment of the invention. In the example shown in FIG. 7, theapparatus 200 performs the operations from S710-S750, but the apparatus200 shown in FIG. 2 is not limited to using this operational flow. Also,the operational flow in FIG. 7 may be performed by a modified apparatusor a different apparatus that differs from the apparatus 200 shown inFIG. 2.

First, the apparatus 200 receives SAW frequency response data andcorresponding Raman spectrum data (S710). For example, the inputinterface 210 of the apparatus 200 may receive SAW frequency responsedata and corresponding Raman spectrum data generated by the SAW sensor108 and Raman spectrometer 114 or associated electronics as describedabove. That is, the input interface 210 may receive SAW frequencyresponse data generated by the SAW sensor 108 of the GC/SAW system shownin FIG. 1, the SAW frequency response data including one or more peakscorresponding respectively to one or more eluted components separatedfrom a sample by the gas chromatograph 100, and may further receiveRaman spectrum data generated by the Raman spectrometer 114 for the oneor more eluted components. The input interface 210 may, as part ofreceiving the data, align, trim, interpolate, etc. the data so that itcorresponds as shown, by way of example, in FIG. 5. The input interface210 may reformat data from multiple sources (e.g. the SAW sensor 108 andthe Raman spectrometer 114) to be in a single format for use by theapparatus 200.

Having received the SAW frequency response data and Raman spectrum data,the apparatus 200 identifies peaks and valleys of the SAW data (S720).For example, the SAW data analyzer 220 of the apparatus 200 may identifythe one or more peaks of the SAW frequency response data correspondingto the eluted components and may further identify one or more valleys ofthe SAW frequency response data by any known method as described above.For each of the found peaks or some subset thereof, the apparatus 200identifies a set of one or more candidate chemicals (S730). For example,the candidate chemical identifier 230 of the apparatus 200 may identifya set of one or more candidate chemicals for an eluted component ofinterest based on the corresponding peak of the SAW frequency responsedata as characterized by the SAW data analyzer 220.

With a set of one or more candidate chemicals having been identified foran eluted component of interest, the apparatus 200, e.g. the chemicalevaluator 250, then determines the chemical identity of the elutedcomponent of interest according to a selected chemical decision mode(S740). Lastly, the apparatus 200 may estimate the mass or otherquantitative measure of the eluted component of interest (S750). Forexample, the mass estimator 270 of the apparatus 200 may estimate themass or other quantitative measure by comparing the corresponding peakof the SAW frequency response data to a calibration curve stored in thecalibration curve storage 280.

FIG. 8 is an example operational flow of step S730 in FIG. 7. For agiven peak of the SAW frequency response data, the apparatus 200 mayidentify a set of one or more candidate chemicals using a retentionindex. For example, the retention index calculator 232 of the candidatechemical identifier 230 may calculate a retention index of the elutedcomponent from the corresponding peak (S810). Then, the retention indexsearch engine 234 may search the retention index database 240 for one ormore matches between the determined retention index and chemicals in theretention index (S820). In this way, the set of one or more candidatechemicals for the peak may consist of each matched chemical in theretention index database 240, e.g. each known chemical havingsubstantially the same retention index as the determined retention indexor having retention indices within a predetermined error range of thedetermined retention index.

FIG. 9A is an example operational flow of step S740 in FIG. 7. First,the apparatus 200 evaluates whether adjacent peaks of the SAW frequencyresponse data are partially overlapping (S910). For example, the SAWdata analyzer 220 of the apparatus 200 may make the evaluation for everypair of adjacent peaks of the SAW frequency response data or for somesubset of interest by the methodology described above. For a given setof adjacent peaks for which the evaluation is made, the SAW dataanalyzer 220 may characterize the adjacent peaks as being partiallyoverlapping (e.g. mark, notate, flag, insert metadata, etc.) if it isevaluated that the adjacent peaks are partially overlapping. If it isevaluated that the adjacent peaks are not partially overlapping, the SAWdata analyzer 220 may characterize the adjacent peaks as non-overlappingor leave in place a default characterization of non-overlapping.

The analyzed SAW frequency response data including the characterizationsmade by the SAW data analyzer 220 may then be passed to the chemicalevaluator 250, which may determine the chemical identity of thecomponent corresponding to a given peak in accordance with a selectedmode. For example, for each peak belonging to a pair of adjacent peaksthat the SAW data analyzer 220 characterized as partially overlapping(“Yes” at S910), the mode selector 256 of the chemical evaluator 250 mayselect partial-overlap decision mode and the chemical evaluator 250 maydetermine the identity of the chemical component according to thepartial-overlap decision mode (S920). Meanwhile, for each peak notbelonging to a pair of adjacent peaks that the SAW data analyzer 220characterized as partially overlapping, or for each peak that the SAWdata analyzer 220 characterized as non-overlapping or leftnon-overlapping by defaulting (“No” at S910), the mode selector 256 ofthe chemical evaluator 250 may select non-overlap decision mode (e.g. bydefault) and the chemical evaluator 250 may determine the identity ofthe chemical component according to the non-overlap decision mode(S930).

The determinations according to the partial-overlap decision mode (S920)and non-overlap decision mode (S930) may fail. That is, there aresituations where the chemical evaluator 250 will not successfullydetermine the identity of the chemical component, such as when no matchis found in the Raman database 260 for a Raman spectrum produced for thepeak as described above. Some portion of these failures may be theresult of the peak actually being a combination of fully overlappedpeaks, which might be resolved by the full-overlap decision mode.Therefore, after the completion of step S920 or step S930, the apparatus200, e.g. the chemical evaluator 250, may determine whether a match hasbeen found in the Raman database 260 corresponding to each peak forwhich an attempt at determining the chemical identity of the componentwas made in step S920 or S930 (S940). For a given peak, if a match wassuccessfully found (“Yes” at S940), the operational flow of FIG. 9A endsfor that peak as the chemical has been identified. On the other hand, ifa match was not successfully found (“No” at S940), the mode selector 256of the chemical evaluator 250 may select full-overlap decision mode andthe chemical evaluator 250 may determine the identity of the chemicalcomponent according to the full-overlap decision mode (S950). Afterattempting to determine the chemical identity of the component by thefull-overlap decision mode, the operational flow of FIG. 9A ends.

FIG. 9B is a conceptual representation of partially overlapped peaks ofthe SAW frequency response data. In an exemplary GC/SAW system like thesystem 10 shown in FIG. 1, chemical components that elute closely intime may coexist at the condensation spot 110 for part of the time thatthey are condensed. For example, after a first component condenses, asecond component may then condense before the first component completelyevaporates. The actual SAW frequency response data in such a situationmay, for example, appear as the union of the two peaks shown in FIG. 9B.In other words, the interior of the two peaks (i.e. the front of thesecond peak and the tail of the first peak) are not seen in the data andare only included in FIG. 9B for conceptual understanding. As describedabove, the SAW data analyzer 220 may characterize peaks such as those inFIG. 9B as being partially overlapped by evaluating whether the heightof the valley between the peaks exceeds a valley height threshold orwhether the peaks are closer together than a peak distance threshold. Ascan be understood from FIG. 9B, the valley between adjacent peaks mayexceed a valley height threshold in a case of partially overlappingpeaks because the valley does not represent an absence of condensedmaterial on the SAW sensor 108. Rather, the valley occurs at a time whenboth components are accumulated on the SAW sensor 108 (the firstcomponent evaporating as the second component condenses), resulting in asignificant SAW frequency response.

The exemplary representation of partially overlapped peaks is simplifiedin FIG. 9B, in that measured SAW frequency response data may exhibitsome distortion of the peak shape in regions of overlap. Theaccumulation of the mass of the second component may inflate theapparent mass of the first component, and vice versa, and may, in somecases, even shift the first and second peaks. An error range foridentifying a set of candidate chemicals for each peak, e.g., an errorrange when looking up a determined retention index in the retentionindex database 240, may help avoid complications caused by distortionsof this kind.

FIG. 9C is a conceptual representation of fully overlapped peaks of theSAW frequency response data. In an exemplary GC/SAW system like thesystem 10 shown in FIG. 1, chemical components that elute closely intime may coexist at the condensation spot 110 for a long enough durationof the time they are condensed that no valley is detected between thepeaks. The actual SAW frequency response data in such a situation mayappear as the single peak shown in FIG. 9C, which may beindistinguishable from a single peak resulting from a single elutedcomponent. As described above, the SAW data analyzer 220 maycharacterize peaks such as those in FIG. 9C as not being partiallyoverlapped, or as being non-overlapped, by evaluating whether the heightof the valley between the apparently single peak and an adjacent peak(not shown) exceeds a valley height threshold or whether the apparentlysingle peak is closer to an adjacent peak than a peak distancethreshold. In this case, there is no peak near the apparently singlepeak of FIG. 9C, so the SAW data analyzer 220 may characterize theapparently single peak as not partially overlapped or as non-overlapped(or leave the peak uncharacterized to be interpreted as non-overlappedby default).

As noted above, the overlapping of peaks may distort the shape of thepeaks as the accumulation of the mass of the second component inflatesthe apparent mass of the first component, and vice versa. The closer thepeaks are together, the more likely this effect will eliminate anyvalley between the peaks, as the combined mass of both chemicalcomponents results in a greater SAW frequency response than that ofeither of the two peaks. Since there is no valley in this situation, theresult is fully overlapped peaks as shown in FIG. 9C, though theapparently single peak may have a distorted shape. The error range foridentifying a set of candidate chemicals for each peak, e.g. the errorrange when looking up a determined retention index in the retentionindex database 240, may be set to be wide enough so that the chemicalsof the adjacently eluted components are included in the set of candidatechemicals returned by the candidate chemical identifier 230 for thesingle distorted peak.

FIG. 9D is another example operational flow of step S740 in FIG. 7. Inthe example of FIG. 9D, the order of steps S910 and S940 are reversed.That is, it is first evaluated whether a match has been found in theRaman database 260 corresponding to each peak (S940), and thereafter itis evaluated whether adjacent peaks of the SAW frequency response dataare overlapping (S910). As noted above, one of the reasons forevaluating whether adjacent peaks are partially overlapped peaks is thatsuch peaks may be so close together that accurate Raman spectra of thechemical components may be difficult to produce. Thus, if the SAW dataanalyzer 220 does not seek partially overlapped peaks, it is likely thatthe chemical evaluator 250 will not find Raman matches for those peaksthat are too close together (i.e. the peaks that should have beencharacterized as partially overlapped peaks). More specifically, thespectrum producer 252 may still produce a Raman spectrum by integratingvalley-to-valley for each peak in accordance with the non-overlapdecision mode, but the Raman search engine 254 will likely not findmatches in the Raman database 260 because the produced Raman spectrawill be too distorted by the adjacent chemical component. On the otherhand, depending on the threshold(s) for evaluating adjacent peaks aspartially overlapping, there may be cases where matches could have beenfound and partial-overlap decision mode (described in more detail below)was unnecessary. The operational flow of FIG. 9D may skip this initialevaluation of partially overlapped peaks by the SAW data analyzer 220and instead initially assumes that all peaks are non-overlapping.

Specifically, the operational flow of FIG. 9D begins with the chemicalevaluator 250 determining the identity of each chemical component ofinterest according to the non-overlap decision mode (S930). Then, thechemical evaluator 250 may evaluate whether a match has been found inthe Raman database 260 corresponding to each peak for which an attemptat determining the chemical identity of the component was made in stepS930 (S940). For a given peak, if a match was successfully found (“Yes”at S940), the operational flow of FIG. 9D ends for that peak as thechemical has been identified. If, on the other hand, a match was notsuccessfully found (“No” at S940), the apparatus 200 (e.g. the SAW dataanalyzer 220) may then evaluate whether the unmatched peak belongs to apair of adjacent peaks of the SAW frequency response data that arepartially overlapping (S910). For each peak belonging to a pair ofadjacent peaks that the SAW data analyzer 220 characterized as partiallyoverlapping (“Yes” at S910), the mode selector 356 of the chemicalevaluator 250 may select partial-overlap decision mode and the chemicalevaluator 250 may determine the identity of the chemical componentaccording to the partial-overlap decision mode (S920). Meanwhile, foreach peak not belonging to a pair of adjacent peaks that the SAW dataanalyzer 220 characterized as partially overlapping (“No” at S910), themode selector 256 of the chemical evaluator 250 may select full-overlapdecision mode and the chemical evaluator 250 may determine the identityof the chemical component according to the full-overlap decision mode(S950). After attempting to determine the chemical identity of thecomponent by the partial-overlap decision mode or the full-overlapdecision mode, the operational flow of FIG. 9D ends.

In addition to FIGS. 9A and 9D, there may be other operational flows bywhich the chemical decision modes can be selected and implemented instep S740 of FIG. 7. For instance, considering the possibility of fullyoverlapping peaks within a pair of partially overlapping peaks, theoperational flow may be modified to loop back and repeat or combinemultiple chemical decision modes as the situation may demand or inaccordance with a tradeoff between accuracy and efficiency. Also, theoperational flow of step S740 may further include a final evaluation ofwhether a match was found for a given peak. This information may beused, e.g. by the output interface 290 as part of the output of theapparatus 200. For example, match successes and failures may containuseful information for purposes of error reporting or adjusting of thesetup parameters of the system components as described above (includingadjusting the temperature of the SAW sensor 108 using the thermoelectriccooler 112).

FIG. 10 is an example operational flow of step S930 in FIGS. 9A and 9D.That is, FIG. 10 is an example of determining the chemical identity ofan eluted component of interest in accordance with the non-overlapdecision mode, e.g. upon selection of the non-overlap decision mode fora given peak by the mode selector 256. First, the spectrum producer 252may integrate the Raman spectrum data from valley to valley, i.e. from avalley immediately preceding the peak corresponding to the elutedcomponent of interest to a valley immediately following the peakcorresponding to the eluted component of interest (S1010). Then, theRaman search engine 254 searches the Raman database 260 for a matchbetween the Raman spectrum produced by the spectrum producer 252 and achemical in the Raman database 260 from among the set of candidatechemicals for the eluted component of interest (S1020).

FIG. 11A is an example operational flow of step S920 in FIGS. 9A and 9D.That is, FIG. 11 is an example of determining the chemical identity ofan eluted component of interest in accordance with the partial-overlapdecision mode, e.g. upon selection of the partial-overlap decision modefor a given pair of adjacent peaks by the mode selector 256. First, thespectrum producer 252 integrates the Raman spectrum data to produce afront or tail Raman spectrum (S1110). Specifically, the spectrumproducer 252 may integrate the Raman spectrum data from a valleyimmediately preceding the first of the adjacent peaks to a point priorto a valley between the adjacent peaks, thereby producing a front Ramanspectrum of the first of the adjacent peaks. Alternatively, the spectrumproducer 252 may integrate the Raman spectrum data from a point afterthe valley between the adjacent peaks to a valley immediately followingthe second of the adjacent peaks, thereby producing a tail Ramanspectrum of the second of the adjacent peaks. With reference to FIG. 9B,the point prior to the valley between the adjacent peaks may be anypoint to the left of the valley between the peaks (but still within thefirst peak). Likewise, the point after the valley between the adjacentpeaks may be any point to the right of the valley between the peaks (butstill within the second peak). Thus, unlike the non-overlap decisionmode, where integration of the first peak would be from valley to valley(i.e. all the way to the valley between the peaks), the partial-overlapdecision mode involves the spectrum producer 252 integrating only afront of the first peak to produce a front Raman spectrum. Likewise,unlike the non-overlap decision mode, where integration of the secondpeak would be from valley to valley (i.e. beginning at the valleybetween the peaks), the partial-overlap decision mode involves thespectrum producer 252 integrating only a tail of the second peak toproduce a tail Raman spectrum.

The point prior to the valley between the adjacent peaks and/or thepoint after the valley between the adjacent peaks may be selected basedon, for example, a predetermined front/tail length, a predetermineddistance from the valley between the peaks, a predetermined fraction orpercentage of the peak, etc. The selection criteria may depend on thedistance between the peaks or the height of the valley between thepeaks, as determined by the SAW data analyzer 220 when evaluatingwhether the peaks are partially overlapping. For example, it may benecessary to use a smaller front or tail for peaks that are moreoverlapped. The selection of the point may depend on many other factors,such as the signal-to-noise ratio, the integration time needed toproduce a Raman spectrum, the condensation time or temperature of theSAW sensor 108, etc. It is contemplated that the point prior to thevalley and the point after the valley, and/or the selection criteria,may be adjusted by a user and/or adjusted automatically by the apparatus200 based on any combination of relevant factors, with the goal being toproduce a front or tail Raman spectrum by which a match can be found. Aniterative approach (e.g. iterated automatically) is also possible, wherelarger and larger or smaller and smaller front/tail Raman spectra aregenerated until a Raman match is found.

With a front Raman spectrum of the first peak or a tail Raman spectrumof the second peak having been produced in step S1110, the spectrumproducer 252 then (before, after, or concurrently with step S1110)integrates the Raman spectrum data of the other peak from valley tovalley, thus producing a distorted Raman spectrum of the other peak(S1120). For example, in a case where a front Raman spectrum of thefirst peak is produced in step S1110, the spectrum producer 252 mayintegrate the Raman spectrum data from the valley between the adjacentpeaks to the valley immediately following the second of the adjacentpeaks, thereby producing a distorted Raman spectrum of the second of theadjacent peaks. Or, in a case where a tail Raman spectrum of the secondpeak is produced in step S1110, the spectrum producer may integrate theRaman spectrum data from the valley immediately preceding the first ofthe adjacent peaks to the valley between the adjacent peaks, therebyproducing a distorted Raman spectrum of the first of the adjacent peaks.Such Raman spectra are referred to as “distorted” because, due to thepartially overlapping nature of the peaks, they are likely to suffersignificant influence from the nearby peak making a direct Raman matchof the distorted Raman spectrum difficult.

Thus, at the end of step S1120, the spectrum producer 252 has producedeither a front Raman spectrum of the first peak and a distorted Ramanspectrum of the second peak, or a tail Raman spectrum of the second peakand a distorted Raman spectrum of the first peak. Next, the spectrumproducer 252 may subtract the front or tail Raman spectrum from thedistorted Raman spectrum, thereby producing a corrected Raman spectrum(S1130). For example, in the case where the spectrum producer 252 hasproduced a front Raman spectrum of the first peak and a distorted Ramanspectrum of the second peak, the spectrum producer 252 may subtract theproduced front Raman spectrum of the first peak from the distorted Ramanspectrum of the second peak, thereby producing a corrected Ramanspectrum of the second peak. Or, in the case where the spectrum producer252 has produced a tail Raman spectrum of the second peak and adistorted Raman spectrum of the first peak, the spectrum producer 252may subtract the produced tail Raman spectrum of the second peak fromthe distorted Raman spectrum of the first peak, thereby producing acorrected Raman spectrum of the first peak.

Lastly, with sets of candidate chemicals for the eluted componentscorresponding to the first and second peaks having been identified bythe candidate chemical identifier 230, the Raman search engine 254searches the Raman database 260 for matches among the candidatechemicals(s) using the front/tail and/or corrected Raman spectra(S1140). For example, in a case where the spectrum producer 252 hasproduced a front Raman spectrum of the first peak and a corrected Ramanspectrum of the second peak, the Raman search engine 254 may search theRaman database 260 for a match between the produced front Raman spectrumand a chemical in the Raman database 260 from among the set of candidatechemicals for the eluted component corresponding to the first of theadjacent peaks. Instead, or additionally (depending on which peak orpeaks are of interest), the Raman search engine 254 may search the Ramandatabase 260 for a match between the corrected Raman spectrum of thesecond of the adjacent peaks and a chemical in the Raman database 260from among the set of candidate chemicals for the eluted componentcorresponding to the second of the adjacent peaks. On the other hand, ina case where the spectrum producer 252 has produced a tail Ramanspectrum of the second peak and a corrected Raman spectrum of the firstpeak, the Raman search engine 254 may search the Raman database 260 fora match between the produced tail Raman spectrum and a chemical in theRaman database 260 from among the set of candidate chemicals for theeluted component corresponding to the second of the adjacent peaks.Instead, or additionally (depending on which peak or peaks are ofinterest), the Raman search engine 254 may search the Raman database 260for a match between the corrected Raman spectrum of the first of theadjacent peaks and a chemical in the Raman database 260 from among theset of candidate chemicals for the eluted component corresponding to thefirst of the adjacent peaks. In this way, the partially overlappingpeaks may be effectively separated and the chemical componentcorresponding to one or both peaks may be identified.

FIG. 11B is another example operational flow of step S920 in FIGS. 9Aand 9D. In the example of FIG. 11B, the spectrum producer 252 does notproduce a corrected spectrum but instead produces both a front spectrumof the first peak and a tail spectrum of the second peak. First, thespectrum producer 252 integrates the Raman spectrum data to produce afront Raman spectrum of the first peak (S1110A). Specifically, thespectrum producer 252 may integrate the Raman spectrum data from avalley immediately preceding the first of the adjacent peaks to a pointprior to a valley between the adjacent peaks, thereby producing a frontRaman spectrum of the first of the adjacent peaks. Then (before, after,or concurrently with step S1110A), the spectrum producer 252 integratesthe Raman spectrum data to produce a tail Raman spectrum of the secondpeak (S1110B). Specifically, the spectrum producer 252 may integrate theRaman spectrum data from a point after the valley between the adjacentpeaks to a valley immediately following the second of the adjacentpeaks, thereby producing a tail Raman spectrum of the second of theadjacent peaks. Lastly, with sets of candidate chemicals for the elutedcomponents corresponding to the first and second peaks having beenidentified by the candidate chemical identifier 230, the Raman searchengine 254 searches the Raman database 260 for matches among thecandidate chemicals(s) using the front and/or tail Raman spectra(S1140). For example, the Raman search engine 254 may search the Ramandatabase 260 for a match between the produced front Raman spectrum and achemical in the Raman database 260 from among the set of candidatechemicals for the eluted component corresponding to the first of theadjacent peaks. Instead, or additionally (depending on which peak orpeaks are of interest), the Raman search engine 254 may search the Ramandatabase 260 for a match between the produced tail Raman spectrum and achemical in the Raman database 260 from among the set of candidatechemicals for the eluted component corresponding to the second of theadjacent peaks. In this way as well, the partially overlapping peaks maybe effectively separated and the chemical component corresponding to oneor both peaks may be identified.

FIG. 12 is an example operational flow of step S950 in FIGS. 9A and 9D.That is, FIG. 12 is an example of determining the chemical identity ofan eluted component of interest in accordance with the full-overlapdecision mode, e.g. upon selection of the full-overlap decision mode fora given peak by the mode selector 256. As discussed above, thefull-overlap decision mode may be selected, for example, when a Ramanmatch is not found using another decision mode. In the non-overlapdecision mode and partial-overlap decision mode, Raman searches by theRaman search engine 254 are conducted to find a chemical for a givenRaman spectrum that has been produced. In the full-overlap decisionmode, the Raman search is conducted “in reverse,” that is, to find aRaman spectrum for a given chemical that has been identified as acandidate chemical by the candidate chemical identifier 230.

First, with a set of candidate chemicals for the apparently single peak(actually more than one fully-overlapped peaks) having been identifiedby the candidate chemical identifier 230, the Raman search engine 254searches the Raman database 260 for known Raman spectra of the one ormore candidate chemicals (S1210). It should be noted that, for purposesof the full-overlap mode, it can be assumed that the one or morecandidate chemicals returned by the candidate chemical identifier 230may include at least two candidate chemicals. If there were only onecandidate chemical, it may be assumed that the apparently single peak isnot a combination of fully-overlapped peaks and bypass the full-overlapmode. Since the candidate chemicals for the peak are those chemicalsidentified for the retention time of the peak (e.g. chemicals having thesame or similar retention index as determined from the peak), the set ofreturned known Raman spectra is understood to include the Raman spectraof the actual eluted components that combined to produce the peak. Inother words, the fully-overlapped peaks, if separable, may be understoodto produce Raman spectra from the set of returned known Raman spectra.Thus, the goal is to find the subset of the returned known Raman spectrathat represent the actual eluted components that combined to produce thepeak.

To achieve the above goal, the spectrum producer 252 decomposes a Ramanspectrum produced for the peak into a combination of the known Ramanspectra returned by the Raman search engine 254 (S1220). As describedearlier (see e.g. FIG. 9A), the spectrum producer 252 is understood tohave produced a Raman spectrum for the peak in accordance with apreviously selected mode. This may be, for example, a valley-to-valleyRaman spectrum produced in accordance with the non-overlap mode, acorrected Raman spectrum produced in accordance with the partial-overlapmode, or a front/tail Raman spectrum produced in accordance with thepartial-overlap mode. In any case, a match with the produced Ramanspectrum has not been found, thereby resulting in the selection offull-overlap mode. Alternatively, the full-overlap mode itself mayinclude the spectrum producer 252 producing a valley-to-valley Ramanspectrum of the apparent single peak. The produced Raman spectrum,whenever and however it is produced, may be decomposed into acombination of the known Raman spectra by any known method, such as by aself-modeling mixture analysis method, a self-modeling curve resolutionmethod, a systematic trial-and-error routine of subtracting the knownRaman spectra from the produced Raman spectrum, or a computationalmethod. By decomposing the produced Raman spectrum into the known Ramanspectra corresponding to the candidate chemicals, the chemical evaluator250 may deduce the chemical identity of the eluted components thatcombined to result in the produced Raman spectrum for that retentiontime. If, on the other hand, it is not possible to decompose theproduced Raman spectrum into the known Raman spectra, full-overlap modefails. There may be some other problem with the system as describedabove.

FIG. 13 is an example operational flow of step S1220 in FIG. 12. First,the spectrum producer 252 subtracts one or more of the known Ramanspectra found in step S1210 of FIG. 12 from the Raman spectrum producedfor the peak by the spectrum producer 252 (e.g. the Raman spectrumproduced by integrating valley to valley in the non-overlap mode)(S1320). The spectrum producer 252 then validates that the Ramanspectrum resulting from the subtraction is itself one of the known Ramanspectra found in step S1210 of FIG. 12 (S1320). If the resulting Ramanspectrum is one of the known Raman spectra (“Yes” at S1320), then thedecomposing is understood to have been successful, and it may beconcluded that the Raman spectrum produced by the spectrum producer 252(for which there was no match) is a combination of the known spectrathat were subtracted from the produced Raman spectrum in step S1310 andthe resulting Raman spectrum that was validated in step S1320. Thevalidation may be performed by matching the Raman spectrum resultingfrom the subtraction with each of the remaining known Raman spectra.Matching can be achieved, for example, in the same way as matching tospectra in the Raman database 260, e.g. such that a difference betweenthe Raman spectrum resulting from the subtraction and the known Ramanspectrum is within a predetermined range.

If, on the other hand, the validation fails (“No” at step S1320), thespectrum producer 252 repeats the process using a different one or moreof the known Raman spectra in the next iteration of step S1310. That is,the spectrum producer 252 increments the one or more known Raman spectrato be subtracted (S1330). For example, if the known Raman spectraconsist of known Raman spectra R_(known)_1, R_(known)_2, R_(known)_3,R_(known)_4, and R_(known)_5, the first iteration of step S1310 may beto subtract R_(known)_1 from the produced Raman spectrum. Aftervalidation fails at step S1320, the spectrum producer 252 may thenincrement to R_(known)_2 at step S1330 and subtract R_(known)_2 from theproduced Raman spectrum at the next iteration of step S1310. Assumingvalidation fails for R_(known)_1, R_(known)_2, R_(known)_3, R_(known)_4,and R_(known)_5 in this way, the spectrum producer 252 may thenincrement to the combination of R_(known)_1 and R_(known)_2 at stepS1330. That is, in the sixth iteration of step S1310, the spectrumproducer 252 may subtract both R_(known)_1 and R_(known)_2 from theproduced Raman spectrum. The pattern of incrementing may continue withsubtracting R_(known)_1 and R_(known)_3, subtracting R_(known)_1 andR_(known)_4, subtracting R_(known)_1 and R_(known)_5, subtractingR_(known)_2 and R_(known)_3, subtracting R_(known)_2 and R_(known)_4,subtracting R_(known)_2 and R_(known)_5, etc., eventually includingcombinations of three or more known Raman spectra. At any time, ifvalidation succeeds at step S1320, the loop ends because a validatedcombination of the known Raman spectra is found. For instance, if thesubtraction of the combination of R_(known)_2 and R_(known)_4 at stepS1310 yields R_(known)_5 as the resulting Raman spectrum at step S1320,it is understood to mean that the resulting Raman spectrum was one ofthe known Raman spectra (“Yes” at S1320). The spectrum producer may thusconclude that the produced Raman spectrum (for which there was no match)is a combination of R_(known)_2, R_(known)_4, and R_(known)_5, all ofwhich are known Raman spectra for chemicals from among the set ofcandidate chemicals identified by the candidate chemical identifier 230.

In this way, the spectrum producer 252 may decompose the Raman spectrumproduced for a peak of interest into a set of known Raman spectracorresponding to candidate chemicals of the peak. The chemical identityof the combination of eluted components corresponding to the peak maythus be identified by the chemical evaluator 250. The operational flowof FIG. 13 represents one of many possible routines for decomposing theproduced Raman spectrum and is simplified for ease of explanation. Inpractice, the simple subtraction routine of FIG. 13 may be a weighteddecomposition, where each of the subtracted known Raman spectra is alsoweighted by some multiplier, and can be solved by known computationalmethods. That is, the task of decomposing the produced Raman spectruminto a combination of R_(known)_1, R_(known)_2, R_(known)_3,R_(known)_4, and R_(known)_5 may be implemented as a mathematicalprocess of finding weight coefficients w₁, w₂, w₃, w₄, and w₅ for theexpressionw₁R_(known)_1+w₂R_(known)_2+w₃R_(known)_3+w₄R_(known)_4+w₅R_(known)_5=R_(produced),where R_(produced) is the spectrum originally produced by the spectrumproducer 252 that did not have a match in the Raman database 260.

It should be noted that, in the case of fully overlapped peaks, it maynot be possible to estimate the mass or other quantitative measuredirectly from the SAW frequency response data as described above (e.g.comparing the peak to a calibration curve). However, quantitativeanalysis can still be achieved by taking into consideration the set ofknown Raman spectra that resulted from the decomposition by the spectrumproducer 252 in step S1220 of FIG. 12. In particular, the weight ratioof the known Raman spectra that compose the originally produced Ramanspectrum can be used to more accurately estimate the mass or otherquantitative measure. For example, if the apparently single peak(combination of fully overlapped peaks) is the result of a combinationof eluted chemical components having known Raman spectra R_(known)_2,R_(known)_4, and R_(known)_5 as in the above example, with w₁=0, w₂=1,w₃=0, w₄=1.2, and w₅=2.9, the ratio of weights w₂=1, w₄=1.2, and w₅=2.9can be applied to the original peak of the SAW frequency response databy the mass determining section 270 before consulting the calibrationcurve storage 280. In this way, the estimated peak area or peak heightproportionately attributable to each chemical component can be used todetermine the mass or other quantitative measure. Similar techniques canbe used to improve the quantitative analysis for partially overlappingpeaks in some cases.

In the examples described above with respect to the apparatus 200, a setof candidate chemicals is identified by GC/SAW retention index matchingto narrow down the Raman search, with the Raman search being conductedwithin the set of candidate chemicals. However, the present disclosureis not limited to this procedure and other procedures may be used withthe system 10 shown in FIG. 1 as well as with the apparatus 200. Forexample, the Raman search may instead be used to narrow down the GC/SAWretention index matching. The qualitative method that is used as the“coarse” identifier may be performed with appropriately large errorthresholds to allow multiple candidate matches, while the qualitativemethod that is used as the “fine” identifier may be performed withsmaller error thresholds to find a single result. Or, rather than usingone qualitative analysis method to narrow down the other, two competingqualitative analyses can be conducted by GC/SAW retention index matchingand Raman spectrum matching, with the chemical identity of eachcomponent being determined by statistical methods.

By using the systems, methods, and apparatuses described herein, it ispossible to identify trace amounts of chemicals, e.g. nanogram chemicalsin a complex sample matrix with limited separation power because theidentification is based on information from the entire molecularstructure instead of fragment information as in the case of gaschromatography/mass spectrometry (GC/MS) systems. For conventional GCsystems, including GC/MS, accurate identification requires very goodseparation to avoid overlaps of peaks. The additional identification byMS is based on recombination of fragments produced by the targetmolecules and the additional fragments from different chemicals mayintroduce information that is not from signal compounds but frommultiple chemicals. Because the systems, methods, and apparatusesdescribed herein can accurately identify chemicals even when they areoverlapped, the requirements of the GC/SAW system can be relaxed, withthe size of the instrument and cost of operation being reducedaccordingly.

FIGS. 14A and 14B show an example of a computer 1400 in which theapparatus 200 of FIG. 2, the operational flows of FIGS. 6-13, and/orother embodiments of the claimed invention may be wholly or partlyembodied. The computer 1400 according to the present embodiment, asshown in FIG. 14A, generally includes a system unit 1410 and a displaydevice 1420. The display device 1420 produces a graphical output fromthe data processing operations performed by the system unit 1410. Inputdevices including a keyboard 1430 and a mouse 1440, for example, may bemanipulated by a user to generate corresponding inputs to the dataprocessing operations, and are connected to the system unit 1410 viaports 1450. Various other input and output devices may be connected tothe system unit 1410, and different interconnection modalities are knownin the art.

As shown in the block diagram of FIG. 14B, the system unit 1410 includesa processor (CPU) 1411, which may be any conventional type. A systemmemory (RAM) 1412 temporarily stores results of the data processingoperations performed by the CPU 1411, and is interconnected theretotypically via a dedicated memory channel 1413. The system unit 1410 mayalso include permanent storage devices such as a hard drive 1414, whichis also in communication with the CPU 1411 over an input/output (I/O)bus 1415. A dedicated graphics module 1416 may also connected to the CPU1411 via a video bus 1417, and transmits signals representative ofdisplay data to the display device 1420. As indicated above, thekeyboard 1430 and the mouse 1440 are connected to the system unit 1410over the ports 1450. In embodiments where the ports 1450 are UniversalSerial Bus (USB) type, there may be a USB controller 1418 thattranslates data and instructions to and from the CPU 1411 for theexternal peripherals connected via the ports 1450 or wirelesslyconnected such as via Bluetooth connectivity. Additional devices such asprinters, microphones, speakers, and the like may be connected to thesystem unit 1410 thereby. The system unit 1410 may utilize any operatingsystem having a graphical user interface (GUI), such as WINDOWS fromMicrosoft Corporation of Redmond, Wash., MAC OS from Apple, Inc. ofCupertino, Calif., various versions of UNIX with the X-Windows windowingsystem, and so forth. The system unit 1410 executes one or more computerprograms, with the results thereof being displayed on the display device1420. Generally, the operating system and the computer programs aretangibly embodied in a computer-readable medium, e.g., the hard drive1414. Both the operating system and the computer programs may be loadedfrom the aforementioned data storage devices into the RAM 1412 forexecution by the CPU 1411. The computer programs may compriseinstructions, which, when read and executed by the CPU 1411, cause thesame to perform or execute the steps or features of the variousembodiments set forth in the present disclosure.

For example, a program that is installed in the computer 1400 can causethe computer 1400 to function as an apparatus such as the apparatus 200of FIG. 2. Such a program may act on the CPU 1411 to cause the computer1400 to function as some or all of the sections, components, elements,databases, engines, interfaces, etc. of the apparatus 200 of FIG. 2(e.g., the candidate chemical identifier 230, the chemical evaluator250, etc.). A program that is installed in the computer 1400 can alsocause the computer 1400 to perform an operational flow such as thoseillustrated in FIGS. 6-13. Such a program may act on the CPU 1411 tocause the computer 1400 to perform some or all of the steps of FIG. 7(e.g., identify candidate chemical(s) S730, determine chemical accordingto chemical decision mode S740, etc.).

The above-mentioned program may be provided to the hard drive 1414 by orotherwise reside on an external storage medium such as a DVD-ROM,optical recording media such as a Blu-ray Disk or a CD, magneto-opticrecording medium such as an MO, a tape medium, a semiconductor memorysuch as an IC card, a mechanically encoded medium such as a punch card,etc. Additionally, program storage media can include a hard disk or RAMin a server system connected to a communication network such as adedicated network or the Internet, such that the program may be providedto the computer 1400 via the network. Program storage media may, in someembodiments, be non-transitory, thus excluding transitory signals perse, such as radio waves or other electromagnetic waves.

Instructions stored on a program storage medium may include, in additionto code executable by a processor, state information for execution byprogrammable circuitry such as a field-programmable gate arrays (FPGA)or programmable logic array (PLA).

Although certain features of the present disclosure are described inrelation to a computer 1400 with input and output capabilities includinga keyboard 1430 and mouse 1440, specifics thereof are presented by wayof example only and not of limitation. Any alternative graphical userinterfaces such as touch interfaces and pen/digitizer interfaces may besubstituted. The analogs of those features will be readily appreciated,along with suitable modifications to accommodate these alternativeinterfaces while still achieving the same functionalities.

Along these lines, the foregoing computer 1400 represents only oneexemplary apparatus of many otherwise suitable for implementing aspectsof the present disclosure, and only the most basic of the componentsthereof have been described. It is to be understood that the computer1400 may include additional components not described herein, and mayhave different configurations and architectures. Any such alternative isdeemed to be within the scope of the present disclosure.

The above description is given by way of example, and not limitation.Given the above disclosure, one skilled in the art could devisevariations that are within the scope and spirit of the innovationsdisclosed herein. Further, the various features of the embodimentsdisclosed herein can be used alone, or in varying combinations with eachother and are not intended to be limited to the specific combinationdescribed herein. Thus, the scope of the claims is not to be limited bythe illustrated embodiments.

1. A method for identification of chemicals in a sample, the methodcomprising: receiving surface acoustic wave frequency response datagenerated by a surface acoustic wave sensor of a gaschromatography/surface acoustic wave system, the surface acoustic wavefrequency response data including one or more peaks correspondingrespectively to one or more eluted components separated from a sample bya gas chromatograph of the gas chromatography/surface acoustic wavesystem; receiving Raman spectrum data generated by a Raman spectrometerfor the one or more eluted components; producing a Raman spectrumcorresponding to an eluted component of interest from among the one ormore eluted components based upon an integration of the Raman spectrumdata; identifying a set of one or more candidate chemicals for theeluted component of interest based on the corresponding peak of thesurface acoustic wave frequency response data; and searching a Ramandatabase for a match between the produced Raman spectrum and a chemicalin the Raman database from among the set of candidate chemicals for theeluted component of interest.
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